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Edge-texture feature based image forgery detection with cross dataset evaluation

Asghar, Khurshid, Sun, Xianfang ORCID:, Rosin, Paul ORCID:, Saddique, Mubbashar, Hussain, Muhammad and Habib, Zulfiqar 2019. Edge-texture feature based image forgery detection with cross dataset evaluation. Machine Vision and Applications 30 (7-8) , pp. 1243-1262. 10.1007/s00138-019-01048-2

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A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs Discriminative robust local binary patterns (DRLBP) for encoding tampering traces and a support vector machine (SVM) for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag (Germany)
ISSN: 0932-8092
Funders: Higher Education Commission (HEC) Pakistan, European Unions Horizon 2020
Related URLs:
Date of First Compliant Deposit: 28 October 2019
Date of Acceptance: 28 August 2018
Last Modified: 07 Nov 2023 06:34

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